Computer Aided Drug and Discovery

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Faculty Mentor:

Wonpil Im, Professor of Bioengineering, Presidential Endowed Chair in Health, Science, and Engineering

Summer 2020 Students:

Grant Armstrong '21, Cognitive Science

Carly Carpino '22, Bioengineering (Biopharmaceutical Track)

Stephen Gee '23, IBE (Integrated Degree in Buiness and Engineering)

Tim Hartnagel '21, Biocomputational Engineering

Nathan Kern 'G, Computer Science and Engineering

Lingyankg Kong '22, Bioengineering (Biopharmaceutical Track)

Danielle Picarello '22, IDEAS (Integrated Degree in Engineering and Arts and Sciences) Bioengineering and Molecular Biology concentrations

Amanda Rubin '22, Bioengineering

Isslam Yehia '23, Computer Science and Engineering

Program Summary:

Despite advances in biotechnology, the number of new drugs approved per billion USD spent on drug research and development has halved roughly every 9 years, indicating declining R&D effieciency. Therefore, the abilit y to conduct effiecient computational drug discovery has emerged as a vital component to improve both the efficiency and economics of drug discovery. Drug compounds bind to proteins, regulating their functions to acquire beneficial effects to treat diseases. Therefore, better understanding of protein-legand interactions at the molecular level, and accurate quantification or prediction of their binding affinity, are at the core if computer-aided drug discovery. This project aims to study protein-ligand interactions computationally, using three families of impactful therapeutic targets for cancers and AIDS: estrogen receptor; HIV-1 protease; and three types of kinases (Ser, Thr, Tyr). Out of a large number if data sets, we will choose a few test cases and compare calculated biding freee energy results with the cooresponding experiment data. In particular, we plan to provide practical hands-on research experiments in computer-aided drug discovery. The lectures and tools in CHARMM-GUI (http://charmm-gui.org/lecture) will be used for student learning and research.